Update app.py

app.py

@@ -75,32 +75,21 @@ else:
     df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
     df_lu = df_speckle_lu.copy()
     df_lu = df_lu.astype(str)
-    df_lu =  df_lu.set_index("uuid", drop=False)
-
+    df_lu =  df_lu.set_index("uuid", drop=False)  # variable, uuid as default
 
     df_dm = matrices[distanceMatrixActivityNodes]
     
-   
-    #matrices_dict = matrices.to_dict('index')
-
-    
-    df_dm = matrices[distanceMatrixActivityNodes]
-    
-
     df_dm_dict = df_dm.to_dict('index')
  
-
     
     # Replace infinity with 10000 and NaN values with 0, then convert to integers
     df_dm = df_dm.replace([np.inf, -np.inf], 10000).fillna(0)
     df_dm = df_dm.apply(pd.to_numeric, errors='coerce')
     df_dm = df_dm.round(0).astype(int)
-    
-    #df_dm_transport = matrices[distanceMatrixTransportStops]
-    #df_dm_transport_dictionary = df_dm_transport.to_dict('index')
-    
+        
     mask_connected = df_dm.index.tolist()
-    lu_columns = []
+    
+    lu_columns = []   # provided by user? or prefix
     for name in df_lu.columns:
       if name.startswith("lu+"):
         lu_columns.append(name)
@@ -132,13 +121,13 @@ def test(input_json):
 
     from config import useGrasshopperData 
 
-    if useGrasshopperData == True:
+    if useGrasshopperData == True: # grasshopper input
         matrix = inputs['input']["matrix"]
-        landuses = inputs['input']["landuse_areas"]    
+        landuses = inputs['input']["landuse_areas"]    # fetch grasshoper data or not
     
         dfLanduses = pd.DataFrame(landuses).T
         dfLanduses = dfLanduses.apply(pd.to_numeric, errors='coerce')
-        dfLanduses = dfLanduses.replace([np.inf, -np.inf], 0).fillna(0)
+        dfLanduses = dfLanduses.replace([np.inf, -np.inf], 0).fillna(0)  # cleaning function?
         dfLanduses = dfLanduses.round(0).astype(int)
 
         dfMatrix = pd.DataFrame(matrix).T
@@ -146,7 +135,7 @@ def test(input_json):
         dfMatrix = dfMatrix.replace([np.inf, -np.inf], 10000).fillna(0)
         dfMatrix = dfMatrix.round(0).astype(int)
     else:
-        dfLanduses = df_lu_filtered.copy()     
+        dfLanduses = df_lu_filtered.copy()     # fetch speckl data or not
         dfMatrix = df_dm.copy()
         
 
@@ -154,7 +143,7 @@ def test(input_json):
     dm_dictionary = dfMatrix.to_dict('index')
 
     attributeMapperDict_gh = inputs['input']["attributeMapperDict"]
-    landuseMapperDict_gh = inputs['input']["landuseMapperDict"]
+    landuseMapperDict_gh = inputs['input']["landuseMapperDict"]        # if fetch notion data or not, def
 
 
     from config import alpha as alphaDefault
@@ -174,15 +163,10 @@ def test(input_json):
 
         
           
-    
-
-
-    from imports_utils import splitDictByStrFragmentInColumnName
-
- 
+    #from imports_utils import splitDictByStrFragmentInColumnName
 
     # create a mask based on the matrix size and ids, crop activity nodes to the mask
-    mask_connected = dfMatrix.index.tolist()
+    #mask_connected = dfMatrix.index.tolist()
 
     valid_indexes = [idx for idx in mask_connected if idx in dfLanduses.index]
     # Identify and report missing indexes
@@ -222,8 +206,8 @@ def test(input_json):
     #AccessibilityInputs = pd.concat([subdomainsAccessibility, artAccessibility,gmtAccessibility], axis=1)
         
 
-    if 'jobs' not in subdomainsAccessibility.columns:
-        print("Error: Column 'jobs' does not exist in the subdomainsAccessibility.")
+    #if 'jobs' not in subdomainsAccessibility.columns:
+    #    print("Error: Column 'jobs' does not exist in the subdomainsAccessibility.")
 
     livability = accessibilityToLivability(dfMatrix,subdomainsAccessibility,livabilityMapperDict,domainsUnique)